Overview

Dataset statistics

Number of variables21
Number of observations17908
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 MiB
Average record size in memory168.0 B

Variable types

Numeric17
Categorical4

Alerts

risk_score_4 is highly overall correlated with risk_score_5High correlation
risk_score_5 is highly overall correlated with risk_score_4High correlation
months_employed has 13201 (73.7%) zerosZeros
years_employed has 694 (3.9%) zerosZeros
current_address_year has 1486 (8.3%) zerosZeros
personal_account_m has 263 (1.5%) zerosZeros
personal_account_y has 398 (2.2%) zerosZeros

Reproduction

Analysis started2023-11-07 18:42:16.610816
Analysis finished2023-11-07 18:42:54.793326
Duration38.18 seconds
Software versionydata-profiling vv4.6.1
Download configurationconfig.json

Variables

entry_id
Real number (ℝ)

Distinct17888
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5596977.6
Minimum1111398
Maximum9999874
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:54.908854image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1111398
5-th percentile1571268.4
Q13378998.8
median5608376
Q37805624.2
95-th percentile9563219.1
Maximum9999874
Range8888476
Interquartile range (IQR)4426625.5

Descriptive statistics

Standard deviation2562472.8
Coefficient of variation (CV)0.45783152
Kurtosis-1.2008191
Mean5596977.6
Median Absolute Deviation (MAD)2214168.5
Skewness-0.015730158
Sum1.0023068 × 1011
Variance6.5662666 × 1012
MonotonicityNot monotonic
2023-11-07T13:42:55.080477image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6848675 2
 
< 0.1%
5946902 2
 
< 0.1%
4455352 2
 
< 0.1%
6561306 2
 
< 0.1%
5896278 2
 
< 0.1%
6739041 2
 
< 0.1%
8156839 2
 
< 0.1%
3903643 2
 
< 0.1%
5659209 2
 
< 0.1%
4235576 2
 
< 0.1%
Other values (17878) 17888
99.9%
ValueCountFrequency (%)
1111398 1
< 0.1%
1111512 1
< 0.1%
1111600 1
< 0.1%
1112315 1
< 0.1%
1112537 1
< 0.1%
1112907 1
< 0.1%
1114070 1
< 0.1%
1114089 1
< 0.1%
1114268 1
< 0.1%
1114275 1
< 0.1%
ValueCountFrequency (%)
9999874 1
< 0.1%
9999421 1
< 0.1%
9998678 1
< 0.1%
9997871 1
< 0.1%
9997796 1
< 0.1%
9997128 1
< 0.1%
9997079 1
< 0.1%
9995957 1
< 0.1%
9995338 1
< 0.1%
9995118 1
< 0.1%

age
Real number (ℝ)

Distinct72
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.015412
Minimum18
Maximum96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:55.243974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile25
Q134
median42
Q351
95-th percentile63
Maximum96
Range78
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.873107
Coefficient of variation (CV)0.27601983
Kurtosis-0.36032326
Mean43.015412
Median Absolute Deviation (MAD)9
Skewness0.31648274
Sum770320
Variance140.97067
MonotonicityNot monotonic
2023-11-07T13:42:55.414769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 570
 
3.2%
43 559
 
3.1%
38 547
 
3.1%
39 546
 
3.0%
44 543
 
3.0%
34 531
 
3.0%
42 522
 
2.9%
40 520
 
2.9%
45 515
 
2.9%
46 513
 
2.9%
Other values (62) 12542
70.0%
ValueCountFrequency (%)
18 31
 
0.2%
19 47
 
0.3%
20 73
 
0.4%
21 77
 
0.4%
22 143
0.8%
23 185
1.0%
24 225
1.3%
25 241
1.3%
26 291
1.6%
27 335
1.9%
ValueCountFrequency (%)
96 1
 
< 0.1%
89 1
 
< 0.1%
87 1
 
< 0.1%
86 3
 
< 0.1%
85 3
 
< 0.1%
84 6
< 0.1%
83 2
 
< 0.1%
82 4
 
< 0.1%
81 3
 
< 0.1%
80 14
0.1%

pay_schedule
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
bi-weekly
10716 
weekly
3696 
semi-monthly
2004 
monthly
1492 

Length

Max length12
Median length9
Mean length8.5499218
Min length6

Characters and Unicode

Total characters153112
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbi-weekly
2nd rowweekly
3rd rowweekly
4th rowbi-weekly
5th rowsemi-monthly

Common Values

ValueCountFrequency (%)
bi-weekly 10716
59.8%
weekly 3696
 
20.6%
semi-monthly 2004
 
11.2%
monthly 1492
 
8.3%

Length

2023-11-07T13:42:55.757716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:42:55.898005image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
bi-weekly 10716
59.8%
weekly 3696
 
20.6%
semi-monthly 2004
 
11.2%
monthly 1492
 
8.3%

Most occurring characters

ValueCountFrequency (%)
e 30828
20.1%
l 17908
11.7%
y 17908
11.7%
w 14412
9.4%
k 14412
9.4%
i 12720
8.3%
- 12720
8.3%
b 10716
 
7.0%
m 5500
 
3.6%
o 3496
 
2.3%
Other values (4) 12492
8.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 140392
91.7%
Dash Punctuation 12720
 
8.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 30828
22.0%
l 17908
12.8%
y 17908
12.8%
w 14412
10.3%
k 14412
10.3%
i 12720
9.1%
b 10716
 
7.6%
m 5500
 
3.9%
o 3496
 
2.5%
n 3496
 
2.5%
Other values (3) 8996
 
6.4%
Dash Punctuation
ValueCountFrequency (%)
- 12720
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 140392
91.7%
Common 12720
 
8.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 30828
22.0%
l 17908
12.8%
y 17908
12.8%
w 14412
10.3%
k 14412
10.3%
i 12720
9.1%
b 10716
 
7.6%
m 5500
 
3.9%
o 3496
 
2.5%
n 3496
 
2.5%
Other values (3) 8996
 
6.4%
Common
ValueCountFrequency (%)
- 12720
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 153112
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 30828
20.1%
l 17908
11.7%
y 17908
11.7%
w 14412
9.4%
k 14412
9.4%
i 12720
8.3%
- 12720
8.3%
b 10716
 
7.0%
m 5500
 
3.6%
o 3496
 
2.3%
Other values (4) 12492
8.2%

home_owner
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
0
10294 
1
7614 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Length

2023-11-07T13:42:56.042919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:42:56.165538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Most occurring characters

ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Most occurring scripts

ValueCountFrequency (%)
Common 17908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10294
57.5%
1 7614
42.5%

income
Real number (ℝ)

Distinct2284
Distinct (%)12.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3657.2147
Minimum905
Maximum9985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:56.306935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum905
5-th percentile1705
Q12580
median3260
Q34670
95-th percentile6585
Maximum9985
Range9080
Interquartile range (IQR)2090

Descriptive statistics

Standard deviation1504.8901
Coefficient of variation (CV)0.4114853
Kurtosis0.86031309
Mean3657.2147
Median Absolute Deviation (MAD)917.5
Skewness0.97023786
Sum65493400
Variance2264694.1
MonotonicityNot monotonic
2023-11-07T13:42:56.470213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3270 62
 
0.3%
3255 61
 
0.3%
3055 60
 
0.3%
3210 58
 
0.3%
3265 58
 
0.3%
3145 57
 
0.3%
3090 56
 
0.3%
3165 56
 
0.3%
3260 55
 
0.3%
3135 54
 
0.3%
Other values (2274) 17331
96.8%
ValueCountFrequency (%)
905 1
 
< 0.1%
1015 2
< 0.1%
1030 1
 
< 0.1%
1055 3
< 0.1%
1090 1
 
< 0.1%
1095 1
 
< 0.1%
1130 1
 
< 0.1%
1140 1
 
< 0.1%
1145 1
 
< 0.1%
1150 1
 
< 0.1%
ValueCountFrequency (%)
9985 1
< 0.1%
9970 1
< 0.1%
9925 1
< 0.1%
9915 1
< 0.1%
9885 1
< 0.1%
9870 1
< 0.1%
9834 1
< 0.1%
9813 1
< 0.1%
9755 1
< 0.1%
9660 1
< 0.1%

months_employed
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1860063
Minimum0
Maximum11
Zeros13201
Zeros (%)73.7%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:56.601998image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile6
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.4008967
Coefficient of variation (CV)2.0243542
Kurtosis3.2506477
Mean1.1860063
Median Absolute Deviation (MAD)0
Skewness2.0468841
Sum21239
Variance5.7643052
MonotonicityNot monotonic
2023-11-07T13:42:56.733028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 13201
73.7%
5 942
 
5.3%
1 866
 
4.8%
6 842
 
4.7%
2 516
 
2.9%
3 416
 
2.3%
4 309
 
1.7%
9 222
 
1.2%
10 209
 
1.2%
7 199
 
1.1%
Other values (2) 186
 
1.0%
ValueCountFrequency (%)
0 13201
73.7%
1 866
 
4.8%
2 516
 
2.9%
3 416
 
2.3%
4 309
 
1.7%
5 942
 
5.3%
6 842
 
4.7%
7 199
 
1.1%
8 144
 
0.8%
9 222
 
1.2%
ValueCountFrequency (%)
11 42
 
0.2%
10 209
 
1.2%
9 222
 
1.2%
8 144
 
0.8%
7 199
 
1.1%
6 842
4.7%
5 942
5.3%
4 309
 
1.7%
3 416
2.3%
2 516
2.9%

years_employed
Real number (ℝ)

ZEROS 

Distinct17
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5268595
Minimum0
Maximum16
Zeros694
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:56.858390image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2597317
Coefficient of variation (CV)0.64072065
Kurtosis0.87386687
Mean3.5268595
Median Absolute Deviation (MAD)1
Skewness0.91271748
Sum63159
Variance5.1063875
MonotonicityNot monotonic
2023-11-07T13:42:56.995148image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 3856
21.5%
3 3526
19.7%
1 2420
13.5%
6 1990
11.1%
5 1956
10.9%
4 1919
10.7%
0 694
 
3.9%
7 589
 
3.3%
9 376
 
2.1%
10 287
 
1.6%
Other values (7) 295
 
1.6%
ValueCountFrequency (%)
0 694
 
3.9%
1 2420
13.5%
2 3856
21.5%
3 3526
19.7%
4 1919
10.7%
5 1956
10.9%
6 1990
11.1%
7 589
 
3.3%
8 216
 
1.2%
9 376
 
2.1%
ValueCountFrequency (%)
16 2
 
< 0.1%
15 5
 
< 0.1%
14 6
 
< 0.1%
13 9
 
0.1%
12 13
 
0.1%
11 44
 
0.2%
10 287
1.6%
9 376
2.1%
8 216
 
1.2%
7 589
3.3%

current_address_year
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5847107
Minimum0
Maximum12
Zeros1486
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:57.133648image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median3
Q35
95-th percentile9
Maximum12
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7519367
Coefficient of variation (CV)0.76768723
Kurtosis0.040406004
Mean3.5847107
Median Absolute Deviation (MAD)2
Skewness0.90347597
Sum64195
Variance7.5731554
MonotonicityNot monotonic
2023-11-07T13:42:57.273945image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 3628
20.3%
1 2772
15.5%
3 2762
15.4%
4 1994
11.1%
0 1486
8.3%
5 1286
 
7.2%
6 1182
 
6.6%
9 692
 
3.9%
8 652
 
3.6%
7 617
 
3.4%
Other values (3) 837
 
4.7%
ValueCountFrequency (%)
0 1486
8.3%
1 2772
15.5%
2 3628
20.3%
3 2762
15.4%
4 1994
11.1%
5 1286
 
7.2%
6 1182
 
6.6%
7 617
 
3.4%
8 652
 
3.6%
9 692
 
3.9%
ValueCountFrequency (%)
12 11
 
0.1%
11 228
 
1.3%
10 598
 
3.3%
9 692
 
3.9%
8 652
 
3.6%
7 617
 
3.4%
6 1182
6.6%
5 1286
7.2%
4 1994
11.1%
3 2762
15.4%

personal_account_m
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4271834
Minimum0
Maximum11
Zeros263
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:57.406327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median2
Q35
95-th percentile7
Maximum11
Range11
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2164401
Coefficient of variation (CV)0.64672351
Kurtosis-0.23427985
Mean3.4271834
Median Absolute Deviation (MAD)1
Skewness0.79412751
Sum61374
Variance4.9126066
MonotonicityNot monotonic
2023-11-07T13:42:57.536244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 6091
34.0%
1 2782
15.5%
6 2368
 
13.2%
3 1632
 
9.1%
4 1587
 
8.9%
5 1348
 
7.5%
7 1135
 
6.3%
9 377
 
2.1%
0 263
 
1.5%
8 209
 
1.2%
Other values (2) 116
 
0.6%
ValueCountFrequency (%)
0 263
 
1.5%
1 2782
15.5%
2 6091
34.0%
3 1632
 
9.1%
4 1587
 
8.9%
5 1348
 
7.5%
6 2368
 
13.2%
7 1135
 
6.3%
8 209
 
1.2%
9 377
 
2.1%
ValueCountFrequency (%)
11 48
 
0.3%
10 68
 
0.4%
9 377
 
2.1%
8 209
 
1.2%
7 1135
 
6.3%
6 2368
 
13.2%
5 1348
 
7.5%
4 1587
 
8.9%
3 1632
 
9.1%
2 6091
34.0%

personal_account_y
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5033505
Minimum0
Maximum15
Zeros398
Zeros (%)2.2%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:57.662131image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q34
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.9555683
Coefficient of variation (CV)0.55819946
Kurtosis1.7615873
Mean3.5033505
Median Absolute Deviation (MAD)1
Skewness1.1649149
Sum62738
Variance3.8242474
MonotonicityNot monotonic
2023-11-07T13:42:57.798583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3 5490
30.7%
2 3801
21.2%
4 3400
19.0%
1 1295
 
7.2%
7 1125
 
6.3%
5 824
 
4.6%
6 721
 
4.0%
8 584
 
3.3%
0 398
 
2.2%
10 113
 
0.6%
Other values (6) 157
 
0.9%
ValueCountFrequency (%)
0 398
 
2.2%
1 1295
 
7.2%
2 3801
21.2%
3 5490
30.7%
4 3400
19.0%
5 824
 
4.6%
6 721
 
4.0%
7 1125
 
6.3%
8 584
 
3.3%
9 62
 
0.3%
ValueCountFrequency (%)
15 1
 
< 0.1%
14 6
 
< 0.1%
13 4
 
< 0.1%
12 15
 
0.1%
11 69
 
0.4%
10 113
 
0.6%
9 62
 
0.3%
8 584
3.3%
7 1125
6.3%
6 721
4.0%

has_debt
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
1
14244 
0
3664 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Length

2023-11-07T13:42:57.945504image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:42:58.069302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Most occurring characters

ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Most occurring scripts

ValueCountFrequency (%)
Common 17908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14244
79.5%
0 3664
 
20.5%

amount_requested
Real number (ℝ)

Distinct98
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean950.44645
Minimum350
Maximum10200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:58.212518image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum350
5-th percentile400
Q1600
median700
Q31100
95-th percentile2600
Maximum10200
Range9850
Interquartile range (IQR)500

Descriptive statistics

Standard deviation698.54368
Coefficient of variation (CV)0.73496375
Kurtosis24.574112
Mean950.44645
Median Absolute Deviation (MAD)200
Skewness3.5995791
Sum17020595
Variance487963.28
MonotonicityNot monotonic
2023-11-07T13:42:58.385828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600 2503
14.0%
700 2317
12.9%
400 2252
12.6%
800 1715
9.6%
900 1466
8.2%
500 1357
 
7.6%
1100 1094
 
6.1%
1200 896
 
5.0%
1000 477
 
2.7%
550 244
 
1.4%
Other values (88) 3587
20.0%
ValueCountFrequency (%)
350 46
 
0.3%
375 1
 
< 0.1%
400 2252
12.6%
401 13
 
0.1%
425 10
 
0.1%
450 224
 
1.3%
475 3
 
< 0.1%
500 1357
7.6%
501 14
 
0.1%
525 8
 
< 0.1%
ValueCountFrequency (%)
10200 1
 
< 0.1%
10100 4
 
< 0.1%
9900 3
 
< 0.1%
9800 3
 
< 0.1%
8300 1
 
< 0.1%
7900 1
 
< 0.1%
7800 1
 
< 0.1%
6300 1
 
< 0.1%
5800 1
 
< 0.1%
5200 20
0.1%

risk_score
Real number (ℝ)

Distinct1411
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61086.302
Minimum2100
Maximum99750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:58.555576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2100
5-th percentile35450
Q149350
median61200
Q372750
95-th percentile85700
Maximum99750
Range97650
Interquartile range (IQR)23400

Descriptive statistics

Standard deviation15394.255
Coefficient of variation (CV)0.2520083
Kurtosis-0.67746693
Mean61086.302
Median Absolute Deviation (MAD)11700
Skewness-0.022398347
Sum1.0939335 × 109
Variance2.3698309 × 108
MonotonicityNot monotonic
2023-11-07T13:42:58.732415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60300 41
 
0.2%
52800 38
 
0.2%
62850 38
 
0.2%
68100 37
 
0.2%
72450 37
 
0.2%
72750 37
 
0.2%
49350 35
 
0.2%
38100 35
 
0.2%
60000 34
 
0.2%
67950 34
 
0.2%
Other values (1401) 17542
98.0%
ValueCountFrequency (%)
2100 1
< 0.1%
2250 1
< 0.1%
2800 1
< 0.1%
4450 1
< 0.1%
6100 1
< 0.1%
11100 1
< 0.1%
11850 1
< 0.1%
13750 1
< 0.1%
15500 1
< 0.1%
15850 1
< 0.1%
ValueCountFrequency (%)
99750 1
< 0.1%
99600 1
< 0.1%
99550 1
< 0.1%
99450 1
< 0.1%
99300 1
< 0.1%
99200 1
< 0.1%
99150 1
< 0.1%
99000 1
< 0.1%
98950 1
< 0.1%
98900 1
< 0.1%

risk_score_2
Real number (ℝ)

Distinct17475
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.69087762
Minimum0.023258235
Maximum0.99999748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:58.902261image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.023258235
5-th percentile0.53286521
Q10.64099307
median0.6995605
Q30.75288656
95-th percentile0.8201192
Maximum0.99999748
Range0.97673924
Interquartile range (IQR)0.11189349

Descriptive statistics

Standard deviation0.090470393
Coefficient of variation (CV)0.13094996
Kurtosis2.1975586
Mean0.69087762
Median Absolute Deviation (MAD)0.055833193
Skewness-0.89681775
Sum12372.236
Variance0.0081848921
MonotonicityNot monotonic
2023-11-07T13:42:59.081002image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.767216807 3
 
< 0.1%
0.726331933 3
 
< 0.1%
0.738753782 3
 
< 0.1%
0.653989916 3
 
< 0.1%
0.747897479 3
 
< 0.1%
0.712057143 3
 
< 0.1%
0.632270588 3
 
< 0.1%
0.682552101 3
 
< 0.1%
0.70047395 3
 
< 0.1%
0.701526891 2
 
< 0.1%
Other values (17465) 17879
99.8%
ValueCountFrequency (%)
0.023258235 1
< 0.1%
0.049926134 1
< 0.1%
0.064669832 1
< 0.1%
0.1128 1
< 0.1%
0.113934454 1
< 0.1%
0.134801681 1
< 0.1%
0.166305042 1
< 0.1%
0.174494202 1
< 0.1%
0.190769748 1
< 0.1%
0.201940336 1
< 0.1%
ValueCountFrequency (%)
0.999997479 1
< 0.1%
0.999947899 1
< 0.1%
0.999797479 1
< 0.1%
0.988086555 1
< 0.1%
0.977121008 1
< 0.1%
0.974789916 1
< 0.1%
0.973058824 1
< 0.1%
0.950252101 1
< 0.1%
0.949579832 1
< 0.1%
0.947361345 1
< 0.1%

risk_score_3
Real number (ℝ)

Distinct3945
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.87827576
Minimum0.45137143
Maximum0.99902361
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:59.245301image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.45137143
5-th percentile0.7711775
Q10.85088211
median0.88100422
Q30.91260774
95-th percentile0.96409813
Maximum0.99902361
Range0.54765218
Interquartile range (IQR)0.061725624

Descriptive statistics

Standard deviation0.054563192
Coefficient of variation (CV)0.062125353
Kurtosis0.94974677
Mean0.87827576
Median Absolute Deviation (MAD)0.030346561
Skewness-0.58796796
Sum15728.162
Variance0.0029771419
MonotonicityNot monotonic
2023-11-07T13:42:59.414892image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.881183785 27
 
0.2%
0.901227779 26
 
0.1%
0.881049111 25
 
0.1%
0.881127671 25
 
0.1%
0.881105225 25
 
0.1%
0.867548034 25
 
0.1%
0.881116448 24
 
0.1%
0.867536811 24
 
0.1%
0.881195008 24
 
0.1%
0.867693931 23
 
0.1%
Other values (3935) 17660
98.6%
ValueCountFrequency (%)
0.451371431 1
< 0.1%
0.50075193 1
< 0.1%
0.529718082 1
< 0.1%
0.536474232 1
< 0.1%
0.554543006 1
< 0.1%
0.601544263 1
< 0.1%
0.627446579 1
< 0.1%
0.63412417 1
< 0.1%
0.642114832 1
< 0.1%
0.64542557 1
< 0.1%
ValueCountFrequency (%)
0.999023613 1
< 0.1%
0.99901239 1
< 0.1%
0.99893383 1
< 0.1%
0.998832825 1
< 0.1%
0.997890106 1
< 0.1%
0.997845215 1
< 0.1%
0.997833992 1
< 0.1%
0.997777878 1
< 0.1%
0.997755432 2
< 0.1%
0.997744209 1
< 0.1%

risk_score_4
Real number (ℝ)

HIGH CORRELATION 

Distinct17628
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58315456
Minimum0.016724453
Maximum0.97893203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:59.590073image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.016724453
5-th percentile0.36933543
Q10.50020801
median0.58820781
Q30.67239492
95-th percentile0.77885078
Maximum0.97893203
Range0.96220758
Interquartile range (IQR)0.17218691

Descriptive statistics

Standard deviation0.12506128
Coefficient of variation (CV)0.21445649
Kurtosis-0.018106498
Mean0.58315456
Median Absolute Deviation (MAD)0.086387109
Skewness-0.2697301
Sum10443.132
Variance0.015640324
MonotonicityNot monotonic
2023-11-07T13:42:59.765904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.632928125 3
 
< 0.1%
0.596921094 3
 
< 0.1%
0.576989844 3
 
< 0.1%
0.621678906 3
 
< 0.1%
0.620900781 3
 
< 0.1%
0.511171094 3
 
< 0.1%
0.489704687 2
 
< 0.1%
0.454353125 2
 
< 0.1%
0.555190625 2
 
< 0.1%
0.531753125 2
 
< 0.1%
Other values (17618) 17882
99.9%
ValueCountFrequency (%)
0.016724453 1
< 0.1%
0.019622383 1
< 0.1%
0.083435078 1
< 0.1%
0.08881875 1
< 0.1%
0.089404688 1
< 0.1%
0.099060078 1
< 0.1%
0.114007812 1
< 0.1%
0.130995313 1
< 0.1%
0.134152344 1
< 0.1%
0.13685 1
< 0.1%
ValueCountFrequency (%)
0.978932031 1
< 0.1%
0.976803125 1
< 0.1%
0.949421094 1
< 0.1%
0.946853906 1
< 0.1%
0.939939063 1
< 0.1%
0.938035938 1
< 0.1%
0.928945312 1
< 0.1%
0.928267188 1
< 0.1%
0.919130469 1
< 0.1%
0.916398437 1
< 0.1%

risk_score_5
Real number (ℝ)

HIGH CORRELATION 

Distinct17597
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.71825198
Minimum0.15336729
Maximum0.99625985
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:42:59.935372image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.15336729
5-th percentile0.51037165
Q10.63370808
median0.72511254
Q30.80668062
95-th percentile0.90818592
Maximum0.99625985
Range0.84289256
Interquartile range (IQR)0.17297255

Descriptive statistics

Standard deviation0.12069734
Coefficient of variation (CV)0.16804317
Kurtosis-0.34028061
Mean0.71825198
Median Absolute Deviation (MAD)0.086148283
Skewness-0.24975807
Sum12862.456
Variance0.014567847
MonotonicityNot monotonic
2023-11-07T13:43:00.103718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.744025479 3
 
< 0.1%
0.725951957 2
 
< 0.1%
0.478010492 2
 
< 0.1%
0.665671519 2
 
< 0.1%
0.807641329 2
 
< 0.1%
0.719406916 2
 
< 0.1%
0.56994569 2
 
< 0.1%
0.586550021 2
 
< 0.1%
0.808566312 2
 
< 0.1%
0.644437143 2
 
< 0.1%
Other values (17587) 17887
99.9%
ValueCountFrequency (%)
0.153367295 1
< 0.1%
0.160914885 1
< 0.1%
0.171134562 1
< 0.1%
0.190369257 1
< 0.1%
0.214473534 1
< 0.1%
0.214770111 1
< 0.1%
0.253026729 1
< 0.1%
0.258500468 1
< 0.1%
0.314389593 1
< 0.1%
0.334146223 1
< 0.1%
ValueCountFrequency (%)
0.996259854 1
< 0.1%
0.993363402 1
< 0.1%
0.993002308 1
< 0.1%
0.992798658 1
< 0.1%
0.992712235 1
< 0.1%
0.992600142 1
< 0.1%
0.991739335 1
< 0.1%
0.991158333 1
< 0.1%
0.989404204 1
< 0.1%
0.989107285 1
< 0.1%

ext_quality_score
Real number (ℝ)

Distinct17463
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62311244
Minimum0.010184
Maximum0.970249
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:43:00.273132image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.010184
5-th percentile0.3972336
Q10.521735
median0.625944
Q30.72984075
95-th percentile0.843793
Maximum0.970249
Range0.960065
Interquartile range (IQR)0.20810575

Descriptive statistics

Standard deviation0.13972853
Coefficient of variation (CV)0.22424288
Kurtosis-0.26405529
Mean0.62311244
Median Absolute Deviation (MAD)0.10402
Skewness-0.19911491
Sum11158.698
Variance0.019524062
MonotonicityNot monotonic
2023-11-07T13:43:00.445870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.580815 6
 
< 0.1%
0.82401 4
 
< 0.1%
0.663622 3
 
< 0.1%
0.850858 3
 
< 0.1%
0.40358 3
 
< 0.1%
0.491581 3
 
< 0.1%
0.650858 3
 
< 0.1%
0.35234 3
 
< 0.1%
0.657388 3
 
< 0.1%
0.598213 3
 
< 0.1%
Other values (17453) 17874
99.8%
ValueCountFrequency (%)
0.010184 2
< 0.1%
0.012841 1
< 0.1%
0.017339 1
< 0.1%
0.022057 1
< 0.1%
0.025976 1
< 0.1%
0.026695 1
< 0.1%
0.035218 1
< 0.1%
0.045458 1
< 0.1%
0.050427 1
< 0.1%
0.055358 1
< 0.1%
ValueCountFrequency (%)
0.970249 1
< 0.1%
0.966953 1
< 0.1%
0.963647 1
< 0.1%
0.963075 1
< 0.1%
0.958013 1
< 0.1%
0.956421 1
< 0.1%
0.956027 1
< 0.1%
0.954732 1
< 0.1%
0.953618 1
< 0.1%
0.950682 1
< 0.1%

ext_quality_score_2
Real number (ℝ)

Distinct17469
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.62206821
Minimum0.006622
Maximum0.966953
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:43:00.803268image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.006622
5-th percentile0.39609975
Q10.51967675
median0.6229735
Q30.72894
95-th percentile0.84288655
Maximum0.966953
Range0.960331
Interquartile range (IQR)0.20926325

Descriptive statistics

Standard deviation0.1398983
Coefficient of variation (CV)0.22489222
Kurtosis-0.28743432
Mean0.62206821
Median Absolute Deviation (MAD)0.1047615
Skewness-0.17505636
Sum11139.998
Variance0.019571535
MonotonicityNot monotonic
2023-11-07T13:43:00.975294image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.10358 5
 
< 0.1%
0.480815 5
 
< 0.1%
0.850858 5
 
< 0.1%
0.52401 4
 
< 0.1%
0.667731 3
 
< 0.1%
0.615961 3
 
< 0.1%
0.5581 3
 
< 0.1%
0.75134 3
 
< 0.1%
0.525689 3
 
< 0.1%
0.549308 3
 
< 0.1%
Other values (17459) 17871
99.8%
ValueCountFrequency (%)
0.006622 1
< 0.1%
0.010184 1
< 0.1%
0.013346 1
< 0.1%
0.013973 1
< 0.1%
0.022057 2
< 0.1%
0.026695 1
< 0.1%
0.035218 1
< 0.1%
0.045564 1
< 0.1%
0.077332 1
< 0.1%
0.093683 1
< 0.1%
ValueCountFrequency (%)
0.966953 1
< 0.1%
0.964559 1
< 0.1%
0.963647 1
< 0.1%
0.961244 1
< 0.1%
0.960768 1
< 0.1%
0.956965 1
< 0.1%
0.956761 1
< 0.1%
0.953601 1
< 0.1%
0.952916 1
< 0.1%
0.951938 1
< 0.1%

inquiries_last_month
Real number (ℝ)

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4572258
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size140.0 KiB
2023-11-07T13:43:01.122355image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q38
95-th percentile13
Maximum30
Range29
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6730925
Coefficient of variation (CV)0.56883445
Kurtosis5.6711798
Mean6.4572258
Median Absolute Deviation (MAD)2
Skewness1.916539
Sum115636
Variance13.491609
MonotonicityNot monotonic
2023-11-07T13:43:01.272862image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
6 2918
16.3%
5 2798
15.6%
4 2351
13.1%
3 1910
10.7%
7 1796
10.0%
8 1330
7.4%
2 1254
7.0%
9 892
 
5.0%
10 672
 
3.8%
11 497
 
2.8%
Other values (20) 1490
8.3%
ValueCountFrequency (%)
1 7
 
< 0.1%
2 1254
7.0%
3 1910
10.7%
4 2351
13.1%
5 2798
15.6%
6 2918
16.3%
7 1796
10.0%
8 1330
7.4%
9 892
 
5.0%
10 672
 
3.8%
ValueCountFrequency (%)
30 4
 
< 0.1%
29 5
 
< 0.1%
28 11
 
0.1%
27 17
 
0.1%
26 16
 
0.1%
25 11
 
0.1%
24 20
0.1%
23 17
 
0.1%
22 32
0.2%
21 44
0.2%

e_signed
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size140.0 KiB
1
9639 
0
8269 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters17908
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Length

2023-11-07T13:43:01.421587image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-07T13:43:01.543449image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Most occurring characters

ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17908
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Most occurring scripts

ValueCountFrequency (%)
Common 17908
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17908
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9639
53.8%
0 8269
46.2%

Interactions

2023-11-07T13:42:52.204485image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:19.923392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:22.000283image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:24.077865image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:26.039954image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:28.135122image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:30.161937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:32.076570image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:34.191117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:36.164566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:38.107601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:40.080101image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:42.216168image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:44.155658image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:46.154066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:48.072584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:50.250061image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:52.319542image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:20.041827image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:22.116961image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:24.190286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:26.149391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:28.252056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:30.273120image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:32.187360image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:34.306937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:36.273953image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:38.218974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:40.187662image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:42.328236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:44.266223image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:46.262667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:48.182500image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:50.368063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:52.444716image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:20.164575image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:22.243376image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:24.324335image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:26.268298image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:28.375856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:30.393909image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:32.305762image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:34.429291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:36.395293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:38.341712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:40.304912image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:42.452063image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:44.391054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:46.377321image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:48.304559image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:50.488517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:52.555286image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:20.287616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:22.355755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:24.435344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:26.374048image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:28.489745image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:30.502003image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:32.412215image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:34.539729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:36.499808image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:38.454630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:40.416150image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:42.561790image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:44.498359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:46.481807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:48.413532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:50.599049image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:52.671937image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:20.408429image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:22.474531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:24.547755image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:26.484990image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:28.606929image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:30.613244image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:32.520767image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:34.651227image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:36.610606image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:38.569358image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:40.526715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:42.675615image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:44.612815image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:46.591031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:48.525633image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:50.711259image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:52.801682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:20.547692image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:22.603903image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:24.671807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:26.608528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:28.735982image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:30.734560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:32.642021image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:34.779212image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:36.734794image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:38.702730image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:40.651058image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:42.802726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:44.740469image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:46.711394image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:48.652714image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:50.837193image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:52.921698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:20.674978image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:22.732709image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:24.789856image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:26.724113image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:28.856258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:30.845242image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:32.758245image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:34.894995image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:36.851236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:38.827726image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:40.765826image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:42.921616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:44.863846image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:46.827415image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:48.774619image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:50.952234image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:53.037051image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:20.801491image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:22.864008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:24.902645image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:27.009901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:28.972167image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:30.953584image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:32.869565image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:35.009566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:36.960501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:38.942959image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:40.876282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:43.032974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:44.978334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:46.937222image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:48.887525image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:51.064772image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:53.161391image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:20.933913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:22.997610image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:25.024455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:27.129423image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:29.097727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:31.073088image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:32.987066image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:35.129130image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:37.082721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:39.064540image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:40.997042image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:43.152031image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:45.105108image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:47.059590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:49.216590image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:51.184694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:53.278822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:21.050289image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:23.120126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:25.135728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:27.238267image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:29.214758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:31.184345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:33.100919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:35.243507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:37.193038image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:39.180432image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:41.113758image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:43.262660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:45.223652image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:47.170594image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:49.332274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:51.297235image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:53.395431image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:21.179904image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:23.238258image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:25.247263image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:27.350090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:29.331776image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:31.293661image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:33.211940image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:35.354592image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:37.307359image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:39.288538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:41.416873image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:43.373346image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:45.340271image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:47.297338image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:49.448744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:51.407819image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:53.510820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:21.296311image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:23.355707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:25.360402image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:27.459270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:29.448010image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:31.404744image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:33.324681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:35.467760image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:37.418531image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:39.400511image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:41.530906image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:43.482392image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:45.454536image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:47.405291image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:49.562433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:51.521270image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:53.628781image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:21.414621image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:23.473401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:25.477931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:27.572963image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:29.568040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:31.517472image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:33.438596image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:35.583287image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:37.535201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:39.514535image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:41.646497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:43.593252image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:45.571382image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:47.517736image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:49.678569image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:51.634363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:53.746517image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:21.531327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:23.591887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:25.592091image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:27.683913image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:29.687129image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:31.626731image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:33.551410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:35.700203image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:37.647676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:39.626611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:41.761353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:43.704146image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:45.682833image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:47.626497image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:49.791347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:51.747496image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:53.860915image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:21.645362image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:23.709710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:25.699534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:27.795147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:29.803605image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:31.736920image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:33.658710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:35.814925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:37.758126image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:39.739056image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:41.868398image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:43.812777image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:45.798539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:47.734818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:49.903008image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:51.855067image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:53.978177image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:21.762888image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:23.833250image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:25.815367image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:27.906229image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:29.922488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:31.846532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:33.961805image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:35.932336image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:37.871684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:39.851551image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:41.984528image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:43.923746image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:45.919593image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:47.844213image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:50.013918image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:51.970923image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:54.097501image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:21.879354image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:23.952696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:25.925096image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:28.019870image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:30.040572image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:31.959214image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:34.074698image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:36.046327image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:37.988660image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:39.966054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:42.097503image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:44.039251image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:46.037347image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:47.955591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:50.129835image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-07T13:42:52.084933image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-07T13:43:01.656516image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
entry_idageincomemonths_employedyears_employedcurrent_address_yearpersonal_account_mpersonal_account_yamount_requestedrisk_scorerisk_score_2risk_score_3risk_score_4risk_score_5ext_quality_scoreext_quality_score_2inquiries_last_monthpay_schedulehome_ownerhas_debte_signed
entry_id1.0000.005-0.0120.016-0.0040.000-0.001-0.006-0.0040.0070.013-0.0060.0080.0050.0070.006-0.0050.0190.0070.0070.014
age0.0051.0000.187-0.1030.1880.145-0.0160.0430.0880.155-0.0180.1190.0760.0990.0350.0460.0310.1110.1440.0660.094
income-0.0120.1871.000-0.0630.1280.0580.0100.0020.2500.156-0.0250.065-0.0080.021-0.0020.0050.0550.0520.1520.0500.065
months_employed0.016-0.103-0.0631.000-0.263-0.0950.215-0.055-0.018-0.0470.032-0.022-0.018-0.025-0.010-0.005-0.0290.0220.0460.0200.039
years_employed-0.0040.1880.128-0.2631.0000.353-0.0540.1600.0800.089-0.0560.075-0.0310.0010.0220.0330.0160.0380.0330.0540.025
current_address_year0.0000.1450.058-0.0950.3531.0000.0450.1200.0640.074-0.0550.054-0.148-0.0860.0160.0200.0220.0170.1910.0230.009
personal_account_m-0.001-0.0160.0100.215-0.0540.0451.000-0.170-0.045-0.044-0.0220.016-0.052-0.033-0.014-0.009-0.0330.0210.0210.3090.083
personal_account_y-0.0060.0430.002-0.0550.1600.120-0.1701.0000.0460.0130.0270.0680.0030.0350.0300.0220.0120.0220.0790.1620.069
amount_requested-0.0040.0880.250-0.0180.0800.064-0.0450.0461.0000.3280.0150.0500.0460.0590.0110.014-0.0200.0540.0750.0380.171
risk_score0.0070.1550.156-0.0470.0890.074-0.0440.0130.3281.0000.1980.1240.1190.1360.1100.117-0.2350.0330.1370.0230.091
risk_score_20.013-0.018-0.0250.032-0.056-0.055-0.0220.0270.0150.1981.0000.2110.2140.2360.1980.192-0.1730.0250.0130.0410.000
risk_score_3-0.0060.1190.065-0.0220.0750.0540.0160.0680.0500.1240.2111.0000.1750.4670.2390.254-0.0320.0460.0540.0240.060
risk_score_40.0080.076-0.008-0.018-0.031-0.148-0.0520.0030.0460.1190.2140.1751.0000.5910.2240.217-0.0170.2020.1360.0090.010
risk_score_50.0050.0990.021-0.0250.001-0.086-0.0330.0350.0590.1360.2360.4670.5911.0000.2680.2630.0040.1280.0790.0000.019
ext_quality_score0.0070.035-0.002-0.0100.0220.016-0.0140.0300.0110.1100.1980.2390.2240.2681.0000.305-0.0530.0180.0440.0240.041
ext_quality_score_20.0060.0460.005-0.0050.0330.020-0.0090.0220.0140.1170.1920.2540.2170.2630.3051.000-0.0530.0230.0440.0180.031
inquiries_last_month-0.0050.0310.055-0.0290.0160.022-0.0330.012-0.020-0.235-0.173-0.032-0.0170.004-0.053-0.0531.0000.0420.0180.0150.035
pay_schedule0.0190.1110.0520.0220.0380.0170.0210.0220.0540.0330.0250.0460.2020.1280.0180.0230.0421.0000.0460.0890.033
home_owner0.0070.1440.1520.0460.0330.1910.0210.0790.0750.1370.0130.0540.1360.0790.0440.0440.0180.0461.0000.0760.047
has_debt0.0070.0660.0500.0200.0540.0230.3090.1620.0380.0230.0410.0240.0090.0000.0240.0180.0150.0890.0761.0000.038
e_signed0.0140.0940.0650.0390.0250.0090.0830.0690.1710.0910.0000.0600.0100.0190.0410.0310.0350.0330.0470.0381.000

Missing values

2023-11-07T13:42:54.289153image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-07T13:42:54.633293image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

entry_idagepay_schedulehome_ownerincomemonths_employedyears_employedcurrent_address_yearpersonal_account_mpersonal_account_yhas_debtamount_requestedrisk_scorerisk_score_2risk_score_3risk_score_4risk_score_5ext_quality_scoreext_quality_score_2inquiries_last_monthe_signed
0762967340bi-weekly13135033621550362000.7373980.9035170.4877120.5159770.5809180.380918101
1356042861weekly03180063271600301500.7385100.8810270.7134230.8264020.7307200.63072090
2693499723weekly01540600711450345500.6429930.7665540.5950180.7622840.5317120.53171270
3568281240bi-weekly05230061271700421500.6652240.9608320.7678280.7788310.7925520.59255281
4533581933semi-monthly035900522811100538500.6173610.8575600.6134870.6655230.7446340.744634120
5849242321weekly02303058271600748500.6771090.7587650.4956090.6647620.5925560.49255661
6794831326bi-weekly02795044161800508000.7380550.8732040.6664370.7003920.5841300.684130141
7429703643bi-weekly050000211211100691000.7983030.8417470.4019710.5687870.5259050.72590551
8649319132semi-monthly052603031411150640500.6524290.8024330.5938160.5603890.5694590.36945931
9890860551bi-weekly130550611421600597500.6246660.9685650.5099190.7496240.7586070.75860751
entry_idagepay_schedulehome_ownerincomemonths_employedyears_employedcurrent_address_yearpersonal_account_mpersonal_account_yhas_debtamount_requestedrisk_scorerisk_score_2risk_score_3risk_score_4risk_score_5ext_quality_scoreext_quality_score_2inquiries_last_monthe_signed
17898215097639bi-weekly15215052531600382000.7637890.7373410.6015770.6178020.6661750.76617550
17899679934337bi-weekly032650415211200679500.7152180.9114290.6068960.7905310.4316650.53166521
17900710087231weekly03015021220450424500.6437780.9013960.6322840.8562310.6663990.56639961
17901180735544bi-weekly05025623631500545000.7118950.9114180.5225090.7128640.4849130.58491390
17902398322954bi-weekly02620521421600554500.6381830.9730200.5022340.7312390.5795570.67955760
17903994972831monthly03245053261700717000.6911260.9281960.6641120.8380120.7277050.62770520
17904944244246bi-weekly06525021331800518000.6485250.9708320.6992410.8447240.7749180.47491830
17905985759046weekly026850511811200596500.6779750.9181410.6879810.9391010.4720450.67204590
17906870847142bi-weekly02515035611400802000.6427410.8856840.4564480.6868230.4065680.40656831
17907149855929weekly126650410411600649500.7208890.8743720.5055650.6316190.8461630.84616341